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This report is about two of those architectures: Apache™ Hadoop® YARN and Teradata® Aster® Seamless Network Analytical Processing (SNAP) Framework™. In the report, each architecture is described; the use of each in a business problem is illustrated; and the results are compared.

It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.

TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.
Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.

Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage.

This e-book highlights the benefits of Hadoop across several industries and explores how IBM® Biglnsights for Apache™ Hadoop® combines open source Hadoop with enterprise-grade management and analytic capabilities.

A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamlessly integrate with diverse technologies, from data marts to Apache Hadoop systems. And it must automatically discover, protect and monitor sensitive information as part of big data applications.

Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage. An effective big data integration solution delivers simplicity, speed, scalability, functionality and governance to produce consumable data.
To cut through this misinformation and develop an adoption plan for your Hadoop big data project, you must follow a best practices approach that takes into account emerging technologies, scalability requirements, and current resources and skill levels.

Known by its iconic yellow elephant, Apache Hadoop is purpose-built to help companies manage and extract insight from complex and diverse data environments. The scalability and flexibility of Hadoop might be appealing to the typical CIO but Aberdeen's research shows a variety of enticing business-friendly benefits.

This e-book highlights the benefits of Hadoop across several industries and explores how IBM® Biglnsights for Apache™ Hadoop® combines open source Hadoop with enterprise-grade management and analytic capabilities.

Until recently, we used the Intel® Distribution for Apache Hadoop Software (IDH) to support our original three business intelligence (BI) big data use cases, and it delivered results worth millions of dollars to Intel.

Download this whitepaper to learn how Hortonworks Data Platform (HDP), built on Apache Hadoop, offers the ability to capture all structured and emerging types of data, keep it longer, and apply traditional and new analytic engines to drive business value, all in an economically feasible fashion. In particular, organizations are breathing new life into enterprise data warehouse (EDW)-centric data architectures by integrating HDP to take advantage of its capabilities and economics.

This white paper will help you evaluate your ability to protect your data in a Apache Hadoop ecosystem. Read on to learn ten signs that you might need to improve security and data governance in order to manage risk while getting more value out of your Apache Hadoop environment.

The advent of Apache Hadoop™ has led many organizations to replatform their existing architectures to reduce data management costs and find new ways to unlock the value of their data. One area that benefits from replatforming is the data warehouse. According to research firm Gartner, “starting in 2018, data warehouse managers will benefit from hybrid architectures that eliminate data silos by blending current best practices with ‘big data’ and other emerging technology types.” There’s undoubtedly a lot to ain by modernizing data warehouse architectures to leverage new technologies, however the replatforming process itself can be harder than it would at first appear. Hadoop projects are often taking longer than they need to create the promised benefits, and often times problems can be avoided if you know what to avoid from the onset.

This report explores the evolution of big data analytics and its maturity within the enterprise. It discusses the approaches and economics to using a Discovery platform and Apache Hadoop within the same unified analytical architecture.

Learn why advanced analytics tools are essential to sustain a competitive advantage. This white paper reveals seven strategic objectives that can be attained to their full potential only by employing predictive analytics.

If you specialize in relational database management technology, you’ve probably heard a lot about “big data” and the open source Apache Hadoop project. Perhaps you’ve also heard about IBM’s new Big SQL technology, which enables IBM® InfoSphere® BigInsights™ users to query Hadoop data using industry-standard SQL. Curious? This paper introduces you to Big SQL, answering many of the common questions that relational database management system (DBMS) users have about this IBM technology.

In this Gigaom Research webinar, the panel will discuss how the multi-cluster approach can be implemented in real systems, and whether and how it can be made to work. The panel will also talk about best practices for implementing the approach in organizations.

In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.

IBM InfoSphere BigInsights for Hadoop enables organizations to efficiently manage and mine large volumes of diverse data for valuable insights. IBM builds on a 100% Apache Hadoop foundation with common tools such as spreadsheets, R analytics and SQL access for greater usability.

When used effectively, Hadoop can deliver unparalleled value in revealing new analytics-driven revenue streams, improving customer acquisition and retention, as well as increasing operational efficiencies. The Hadoop Buyer's Guide is an invaluable resource for those investigating or evaluating Hadoop---from understanding how Hadoop can solve your data challenges, to what to look for when selecting a solution, to comparing vendors, and preparing for implementation and future success. Download the guide, and get everything you need to know about choosing the right Hadoop distribution for your business success.

This independent whitepaper from the Kusnetzky Group Analyst describes the promise and challenges surrounding Big Data. It also validates the M7 solution from MapR, which simplifies big data management by consolidating disparate solutions into a single, enterprise-ready platform.

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